---
title: "Differential Network Analysis with multiDEGGs"
author: "Elisabetta Sciacca, Myles Lewis"
output:
  html_document:
    toc: true
    toc_float:
      collapsed: false
    toc_depth: 2
    number_sections: false
vignette: >
  %\VignetteIndexEntry{multiDEGGs vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Introduction
The multiDEGGs package performs multi-omic differential network analysis by 
identifying differential interactions between molecular entities (genes, 
proteins, miRNAs, or other biomolecules) across the omic datasets provided.

For each omic dataset, a differential network is constructed, where links 
represent statistically significant differential interactions between entities. 
These networks are then integrated into a comprehensive visualization using 
distinct colors to distinguish interactions from different omic layers. This 
unified visualization allows interactive exploration of cross-omic patterns 
(e.g., differential interactions present at both transcript and protein level). 
For each link, users can access differential statistical significance metrics 
(p-values or adjusted p-values, calculated via robust or traditional linear 
regression with interaction term), and differential regression plots.

Beyond network visualization and exploration, multiDEGGs extends its utility 
into predictive modeling applications. The identified differential interactions 
can be leveraged as engineered features in machine learning pipelines, providing
biologically meaningful predictors that capture relational information 
between molecular entities. The package includes specialized functions for 
nested cross-validation that ensure proper feature selection and engineering 
without data leakage, enabling the construction of robust and interpretable 
predictive models.

## Installation
Install from CRAN:    
`install.packages("multiDEGGs")`
  
Install from Github:  
`devtools::install_github("elisabettasciacca/multiDEGGs")`

## Quick start - Generate Differential Networks

Let's start by loading the package and sample data:

```{r load_data}
library(multiDEGGs)
data("synthetic_metadata")
data("synthetic_rnaseqData")
data("synthetic_proteomicData")
data("synthetic_OlinkData")
```

Generate Differential Networks:

```{r}
assayData_list <- list("RNAseq" = synthetic_rnaseqData,
                       "Proteomics" = synthetic_proteomicData,
                       "Olink" = synthetic_OlinkData)

deggs_object <- get_diffNetworks(assayData = assayData_list,
                                 metadata = synthetic_metadata,
                                 category_variable = "response",
                                 regression_method = "lm",
                                 padj_method = "bonferroni",
                                 verbose = FALSE,
                                 show_progressBar = FALSE,
                                 cores = 2)
```

### Key Parameters of `get_diffNetworks`

It's worth explaining some of the important parameters of `get_diffNetworks`:

* `assayData`: accepts either a single normalized matrix/data frame (for single 
  omic differential analysis), or a list of matrices/data frames (for multi-omic 
  scenarios). For multi-omic analysis, it's highly recommended to use a named 
  list of data. If unnamed, sequential names (assayData1, assayData2, etc.) will 
  be assigned to identify each matrix or data frame.

* `metadata`: can also be a named factor vector, with names matching the patient 
  IDs in column names of the assay data matrices/data frames. In that case, the 
  category_variable can remain unset (NULL by default).

* `category_subset`: this parameter can restrict the analysis to a certain 
  subset of categories available in the metadata/category vector.

* `regression_method`: set to `"lm"` by default because it is faster and highly 
  recommended in machine learning scenarios, where the function might be 
  repeatedly called many times. For basic differential analyses, `"rlm"` can 
  also be used and may perform better in some cases.

* `percentile_vector`: by default, molecular targets (genes, proteins, etc.) 
  whose expression level is below the 35th percentile of the entire data matrix 
  are excluded from the analysis. This threshold can be modified by specifying 
  the percentile vector that is internally used for the percolation analysis. 
  For example, to remove only targets below the 25th percentile, set 
  `percentile_vector = seq(0.25, 0.98, by = 0.05)`.

* `padj_method`: the default method is Bonferroni. Storey's q values often give 
  more generous results but the `qvalue` package needs to be installed first.

**NOTE**: Not all patient IDs need to be present across datasets. Different 
numbers of samples per omic are acceptable. Only IDs whose data is available in 
the colnames of the assayData will be included in the analysis. Missing IDs will 
be listed in a message similar to:

`The following samples IDs are missing in Proteomics: PT001, PT005, PT0030`

## Visualization

The `deggs_object` now contains the differential networks for each omic data
in `assayData_list`. These networks can be integrated into a comprehensive 
visualization where different colors distinguish links from different omic 
layers.

```{r, eval=FALSE}
View_diffNetworks(deggs_object)
``` 

This visualization interface allows you to:

1. Navigate the networks associated with each patient category
2. Filter by link significance
3. Search for specific genes inside the network

<p>&nbsp;</p>
![](multiDEGGs_1.png){width=75%}
<p>&nbsp;</p> 

Thicker links correspond to higher significant p-values.  
The direction of the 
arrows shows the relationship direction reported in literature, not derived from 
the data.

The user can visualize differential regression plots by clicking on a link:

<p>&nbsp;</p>
![](multiDEGGs_2.png){width=75%}
<p>&nbsp;</p> 

Single node differential expressions can also be visualized by clicking on the 
nodes:

<p>&nbsp;</p>
![](multiDEGGs_3.png){width=80%}
<p>&nbsp;</p> 

**NOTE**: For multi-omic scenarios, the data from the first matrix in the list 
passed to `assayData` will be used for this boxplot.

## List All Differential Interactions

Outside of the interactive environment, the `get_multiOmics_diffNetworks()` 
function can be used to get a table of all differential interactions, ordered by 
p-value or adjusted p-value:

```{r, warning=FALSE}
get_multiOmics_diffNetworks(deggs_object, sig_threshold = 0.05)
```
  
   
<p>For single omic scenarios, use the `get_sig_deggs()` function:</p>

```{r}
deggs_object_oneOmic <- get_diffNetworks(assayData = synthetic_rnaseqData,
                                 metadata = synthetic_metadata,
                                 category_variable = "response",
                                 regression_method = "lm",
                                 padj_method = "bonferroni",
                                 verbose = FALSE,
                                 show_progressBar = FALSE,
                                 cores = 2)

get_sig_deggs(deggs_object_oneOmic, sig_threshold = 0.05)
```

## Differential Regression Plots 

To plot the differential regression fits outside of the interactive environment, 
use `plot_regressions()` specifying the omic data to be used and the two targets:
  
```{r, fig.width = 4.5, fig.height = 4, eval=FALSE}
plot_regressions(deggs_object,
                 assayDataName = "RNAseq",
                 gene_A = "MTOR", 
                 gene_B = "AKT2",
                 legend_position = "bottomright")
```
  
<p>&nbsp;</p>
![](plot_regressions.png){width=50%}
  
In single omic analyses, the `assayDataName` parameter can remain unset.

## Differential Network Analysis with More Than Two Groups 

It's possible to compare differential interactions among more than two 
categorical groups. All steps described above stay the same;  
the dropdown 
menu of the interactive environment will show all available categories:

<table>
  <tr>
    <td><img src="multiDEGGs_4.png" width="70%"/></td>
  </tr>
</table>
  
<p> </p>

<p>While regressions and boxplots will show all categories: </p> 
  
   
<table>
  <tr>
    <td><img src="multiDEGGs_5.png" width="100%"/></td>
    <td><img src="multiDEGGs_6.png" width="100%"/></td>
  </tr>
</table>
  
The statistical significance of the interaction term is calculated via one-way 
ANOVA in this case.  
We highly recommend to have at least 4 or 5 observations per group.

## Feature Selection and Engineering with multiDEGGs in Nested Cross-Validation

In computational biology applications involving high-throughput data, 
researchers commonly encounter situations where the number of potential 
predictors far exceeds the available sample size. This dimensional challenge 
requires careful feature selection strategies for both mathematical and clinical
reasons.
  
Standard feature selection methods typically evaluate predictors individually, 
identifying those variables that show the strongest univariate associations with
the outcome variable (such as through t-tests or Wilcoxon tests). 
While effective, this approach overlooks the interconnected nature of biological
systems, where \bold{informative patterns may emerge from relationships between 
variables rather than from individual measurements alone.}
  
Feature engineering represents a complementary strategy that creates new 
predictors by combining or transforming existing variables. In biology, 
such approach can be used to capture higher-order information that reflects the 
interconnected nature of molecular processes. For instance, the ratio between 
two genes may provide more discriminative power than either gene expression 
level independently, particularly when their relative balance is disrupted 
in disease states.
  
The informative content encoded in differential interactions, combined with 
multiDEGGs' ability to identify only literature-validated differential 
relationships, makes it particularly well-suited for both individual feature 
selection and guided creation of engineered predictors in machine learning. 
Such approach has potential to overcome the limitations of conventional 
algorithms which may select individual predictors without clear biological 
significance, compromising both the interpretability and clinical credibility 
of the resulting models. 
  
### Why Nested Cross-Validation for Feature Engineering?

It is crucial that feature selection and modification is conducted exclusively 
on training data within cross-validation loops to prevent information leakage 
from the test set. The `nestedcv` package enables the nested modification of 
predictors within each outer fold, ensuring that the attributes learned from 
the training part are applied to the test data without prior knowledge of the 
test data itself.  
The selected and combined features, and corresponding model, can then be 
evaluated on the hold-out test data without introducing bias.

Both \link[nestedcv](nestcv.glmnet) and \link[nestedcv](nestcv.train) from 
`nestedcv` accept any user-defined function 
that filters or transforms the feature matrix by passing the function name to 
the `modifyX` parameter.  
**The multiDEGGs package provides two specialized functions for this purpose.**

### multiDEGGs_filter(): Pure Differential Network-Based Selection

The `multiDEGGs_filter()` function performs feature selection based entirely on 
differential network analysis. It identifies significant differential molecular 
interactions and can return either the interaction pairs alone or both pairs 
and individual variables involved in those interactions.

#### Key Parameters

When using `multiDEGGs_filter()`, you can control the following parameters 
through `modifyX_options`:

- **`keep_single_genes`** (logical, default `FALSE`): Controls whether to 
include individual genes from significant pairs in addition to the pairs 
themselves
- **`nfilter`** (integer, default `NULL`): Maximum number of predictors to 
return. When `NULL`, all significant interactions found are included

#### Usage Examples

##### Basic Usage: Pairs Only

```{r}
library(nestedcv)
data("synthetic_metadata")
data("synthetic_rnaseqData")

# Regularized linear model with interaction pairs only
fit.glmnet <- nestcv.glmnet(
  y = as.numeric(synthetic_metadata$response),
  x = t(synthetic_rnaseqData),
  modifyX = "multiDEGGs_filter",
  modifyX_options = list(
    keep_single_genes = FALSE,
    nfilter = 20
  ),
  modifyX_useY = TRUE,
  n_outer_folds = 5,
  n_inner_folds = 6,
  verbose = FALSE
)

summary(fit.glmnet)
```

##### Including Individual Genes (keep_single_genes = TRUE)

```{r, fig.width = 3, fig.height = 3}
# Random forest model including both pairs and individual genes
fit.rf <- nestcv.train(
  y = synthetic_metadata$response,
  x = t(synthetic_rnaseqData),
  method = "rf",
  modifyX = "multiDEGGs_filter",
  modifyX_options = list(
    keep_single_genes = TRUE,
    nfilter = 30
  ),
  modifyX_useY = TRUE,
  n_outer_folds = 5,
  n_inner_folds = 6,
  verbose = FALSE
)

fit.rf$summary

# Plot ROC on outer folds
plot(fit.rf$roc)
```

#### How nfilter works with keep_single_genes

- When **`keep_single_genes = FALSE`**: `nfilter` limits only the number of 
interaction pairs returned
- When **`keep_single_genes = TRUE`**: `nfilter` limits the combined count 
of unique individual genes plus interaction pairs. The function prioritizes 
pairs by significance and adds individual genes as needed until the limit is 
reached

### multiDEGGs_combined_filter(): Hybrid Statistical and Network-Based Selection

The `multiDEGGs_combined_filter()` function combines traditional statistical 
feature selection with differential network analysis. This hybrid approach 
allows you to benefit from both conventional univariate selection methods and 
the biological insights from interaction analysis.

#### Key Parameters

- **`filter_method`** (character): Statistical method for single feature 
selection.  
Options: `"ttest"`, `"wilcoxon"`, `"ranger"`, `"glmnet"`, `"pls"`
- **`nfilter`** (integer): Maximum number of features to select
- **`dynamic_nfilter`** (logical): Controls how `nfilter` is applied 
(see detailed explanation below)
- **`keep_single_genes`** (logical): When `dynamic_nfilter = TRUE`, 
determines whether to include individual genes from multiDEGGs pairs

#### Dynamic vs. Balanced Selection Modes

##### Dynamic Selection (`dynamic_nfilter = TRUE`)

In dynamic mode, the function:
1. Selects `nfilter` single genes using the chosen statistical method
2. Adds ALL significant interaction pairs found by multiDEGGs
3. Total predictors = `nfilter` single genes + number of significant pairs

This mode allows the feature space to expand based on the biological complexity 
discovered in each fold.

```{r}
# Dynamic selection with t-test for single genes
fit.dynamic <- nestcv.glmnet(
  y = as.numeric(synthetic_metadata$response),
  x = t(synthetic_rnaseqData),
  modifyX = "multiDEGGs_combined_filter",
  modifyX_options = list(
    filter_method = "ttest", 
    nfilter = 20,
    dynamic_nfilter = TRUE, 
    keep_single_genes = FALSE
  ),
  modifyX_useY = TRUE,
  n_outer_folds = 5,
  n_inner_folds = 6,
  verbose = FALSE
)
```

##### Balanced Selection (`dynamic_nfilter = FALSE`)

In balanced mode, the function:  
1. Allocates approximately half of `nfilter` to interaction pairs  
2. Fills remaining slots with single genes from the statistical filter  
3. Maintains consistent total number of predictors across all folds  

This mode ensures a fixed feature space size while balancing single genes and 
interactions.

```{r}
# Balanced selection with Wilcoxon-test importance
fit.balanced <- nestcv.train(
  y = synthetic_metadata$response,
  x = t(synthetic_rnaseqData),
  method = "rf",
  modifyX = "multiDEGGs_combined_filter",
  modifyX_options = list(
    filter_method = "wilcoxon", 
    nfilter = 40,
    dynamic_nfilter = FALSE
  ),
  modifyX_useY = TRUE,
  n_outer_folds = 5,
  n_inner_folds = 6,
  verbose = FALSE
)
```

#### Available Statistical Methods

- **`"ttest"`**: Two-sample t-test for differential expression
- **`"wilcoxon"`**: Wilcoxon rank-sum test (non-parametric alternative to t-test)
- **`"ranger"`**: Random Forest variable importance scoring (the `ranger` package
must be installed first)
- **`"glmnet"`**: Elastic net regularization coefficients
- **`"pls"`**: Partial Least Squares variable importance

### Practical considerations

Before implementing multiDEGGs in your machine learning pipeline, it's highly 
recommended to first run a preliminary analysis on your complete dataset to 
assess the number of differential interactions detected. This exploratory step 
can guide your choice of approach and parameter settings.

If multiDEGGs identifies only a small number of differential interactions 
(e.g., fewer than 10-20 pairs), these features alone may lack sufficient 
predictive power. In such cases, consider:

- Using `multiDEGGs_combined_filter()` to integrate network-based features with 
traditional statistical selection methods
- Setting `keep_single_genes = TRUE` in `multiDEGGs_filter()` to include 
individual genes involved in the differential pairs
- Adjusting the `percentile_vector` or significance thresholds in the initial 
multiDEGGs analysis to potentially capture more interactions

Conversely, if a large number of differential interactions are detected, 
`multiDEGGs_filter()` alone may provide sufficient feature diversity for 
effective model training.

### Feature Engineering Details

Both functions create ratio-based features from significant gene pairs 
(Gene A / Gene B), which capture the relative expression relationships that 
drive differential network connectivity. The `predict` methods automatically 
handle the feature transformation for both training and test data within each 
cross-validation fold, ensuring no information leakage.

**Note:** If no significant differential interactions are found in a particular 
fold, both functions automatically fall back to t-test-based selection to ensure 
robust performance across all scenarios. This fallback is indicated by a 
printed "0" during execution.


## Citation
```{r}
citation("multiDEGGs")
```